LECT:  topics:

1 intro

2 more intro and more search
    what is AI
    search as problem solving

3 a little more intro; a lot more search
  uniformed search methods
  search space for palindromes

(HURRICANE POSTPONED LECTURE 4)

4 informed search methods
  

Note: game playing (Chapter 5) is optional (but interesting!).  Also
note that we will come back to constraint satisfaction problems
(chapters 3 and 4 have some on this) when we look at temporal
constraint networks/reasoning (a type of knowledge representation).
   (See lecture 7, below)


5 finish informed search methods
  how to design yer own heuristic function!
  more on palindrome search/heuristics

6 (search for adversarial game playing - a little)
  Intro to knowledge rep
  wumpus
  propositional logic
     search for proofs
  intro to temporal logic

7 introduction to machine learning
     i.e., search for meta-solutions
     i.e., self-building knowledge base

8 more on temporal reasoning
     search to satisfy constraints
     example applications

9 constraint satisfaction problems in general
  video on genetic programming

10 knowledge representation concepts
     entailment, inference, monotonicity, etc.
   first-order logic

11 first-order logic examples: Wumpus, others
   
12 inference in first-order logic
   unification

13 rule-based expert systems
   Knowledge Representation Rhapsody

14 last day on logic: resolution, more examples
   midterm review



      ** MIDTERM EXAM **   (Oct 28)


15 midterm solutions
   a last few things about logic
   details of homework #3 -- handed out today
   

16 finish HW3 details and midterm solutions
   intro to planning

17 finish planning -- STRIPS logic
   intro to uncertainty and probability

18 intro to belief networks

19 belief networks: 
       general inference algorithms
       applications

20 more on machine learning: (for HW #5 in particular)
       concept learning
       find-S
       advertisement for S00's W4771 (machine learning)

21 finish up belief networks
   natural language processing (NLP)
       it is an example application of most basic AI techniques:
           search
	   constraint satisfaction
	   machine learning
	   uncertain reasoning
	   planning
	   etc.

22 more NLP
   philosophy of AI

23 overview of AI applications

24 Summary
   Final exam review

FINAL:
W4701                  TUE. 12/21/99       1:10 p.m.    1127 MUD

email: evs at cs dot columbia dot edu